CONTEMPORARY APPROACHES IN THE DIAGNOSIS AND STUDY OF MYCOBACTERIA

THE ROLE OF MICROSCOPY AND ARTIFICIAL INTELLIGENCE

Authors

DOI:

https://doi.org/10.58395/h9830j27

Keywords:

Nontuberculous Mycobacteria, Microscopy, diagnostics

Abstract

The genus Mycobacterium comprises structurally a complex of highly adaptive bacteria of major clinical importance. While tuberculosis remains a leading cause of mortality from infectious diseases worldwide, nontuberculous mycobacteria (NTM) are gaining increasing clinical relevance, particularly among immunocompromised patients. The unique structure of mycobacterial cell wall and the slow growth of these organisms necessitate the use of specialized microscopic approaches for their investigation and diagnosis.

Despite the advances in molecular diagnostics, microscopy remains a rapid and cost-effective key diagnostic tool, however limited by subjectivity and reduced sensitivity at low bacterial loads. The recent integration of artificial intelligence has significantly enhanced light and fluorescence microscopy by enabling an automated and standardized detection of the acid-fast bacilli.

The advanced high-resolution techniques, including electron and cryo-electron microscopy, provide detailed insights into mycobacterial ultrastructure and intracellular behaviour, contributing to a deeper understanding of their pathogenicity and drug resistance. This review summarizes the classical and the modern microscopic approaches and highlights their complementary roles in diagnosistic and fundamental studies of mycobacteria.

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Author Biographies

  • Svetoslav Yordanov, National Centre of Infectious and Parasitic Diseases, Sofia, Bulgaria

    National Reference Laboratory of Tuberculosis (NRL of TB), Department of Microbiology

  • Yuliyana Atanasova, National Centre of Infectious and Parasitic Diseases, Sofia, Bulgaria

    National Reference Laboratory of Tuberculosis (NRL of TB), Department of Microbiology

References

1. Jankute M, Cox JAG, Harrison J, Besra GS. Assembly of the Mycobacterial Cell Wall. Annu Rev Microbiol. 2015;69:405-423. https://doi.org/10.1146/annurev-micro-091014-104121

2. Jarlier V, Nikaido H. Mycobacterial cell wall: structure and role in natural resistance to antibiotics. FEMS Microbiol Lett. 1994;123(1-2):11-18. https://doi.org/10.1111/j.1574-6968.1994.tb07194.x

3. Daffé M. Unraveling the structure of the mycobacterial envelope. Microbiol Spectr. 2015;3(6).

4. Esteban J, García-Coca M. Mycobacterium Biofilms. Front Microbiol. 2018;8:2651. https://doi.org/10.3389/fmicb.2017.02651

5. World Health Organization. Laboratory diagnosis of tuberculosis by sputum microscopy. WHO; 2021.

6. World Health Organization. Global Tuberculosis Report 2023. WHO; 2023.

7. Liu J, Barry CE. Mycolic acid structure determines the fluidity of the mycobacterial cell wall. J Biol Chem. 1996;271(47):29545-29551. https://doi.org/10.1074/jbc.271.47.29545

8. Bhamidi S, Scherman MS, Jones V, et al. Detailed structural and quantitative analysis reveals the spatial organization of the cell walls of in vivo grown Mycobacterium leprae and in vitro grown Mycobacterium tuberculosis. J Biol Chem. 2011;286(27):23168-23179. https://doi.org/10.1074/jbc.M110.210534

9. Chiaradia L, Lefebvre C, Parra J, et al. Dissecting the mycobacterial cell envelope and defining the role of mycomembrane. Sci Rep. 2017;7:12807. https://doi.org/10.1038/s41598-017-12718-4

10. Vijay S, Hai HT, Thu DDA, et al. Ultrastructural analysis of cell envelope and accumulation of lipid inclusions in clinical Mycobacterium tuberculosis isolates. Front Microbiol. 2017;8:2681. https://doi.org/10.3389/fmicb.2017.02681

11. Eskandarian HA, et al. A role for the cell wall in mycobacterial mechanical morphotype switching. Curr Biol. 2017;27(24):4093-4105.

12. Clinical and Laboratory Standards Institute (CLSI). Laboratory Detection and Identification of Mycobacteria. CLSI guideline; 2018.

13. Dufrêne YF. Atomic force microscopy in cellular microbiology: from cell surfaces to single molecules. Cell Microbiol. 2021;23(6):e13324.

14. Marais BJ, Brittle W, Painczyk K, et al. Use of light-emitting diode fluorescence microscopy to detect acid-fast bacilli in sputum. PLoS One. 2011;6(12):e28697

15. Melendez J, Hsieh A, Lam R, et al. Automated detection of Mycobacterium tuberculosis in Ziehl-Neelsen-stained sputum smears using convolutional neural networks. Sci Rep. 2016;6:27327.

16. Shanmugam ST, et al. Deep learning-based automated detection of acid-fast bacilli in digitized microscopy images. PLoS One. 2021;16(4):e0249227.

17. Hoffmann C, Leis A, Niederweis M, et al. Disclosure of the mycobacterial outer membrane: cryo-electron tomography and vitreous sections. Proc Natl Acad Sci U S A. 2008;105(10):3963-3967. https://doi.org/10.1073/pnas.0709530105

18. Tattersfield, A. (2005) Toman's Tuberculosis: Case Detection, Treatment and Monitoring: Questions and Answers, 2nd Edition. Occupational and Environmental Medicine, 62, 70.

19. HANDBOOK FOR THE BACTERIOLOGIC DIAGNOSIS OF TUBERCULOSIS. PART 1: SMEAR MICROSCOPY UPDATE / Program "Strengthening of the Network of Tuberculosis Laboratories in the Region of the Americas" -- Lima: ORAS - CONHU; 2018.

20. Wu Y, Zhou A. In situ, real-time tracking of cell wall topography and nanomechanics of antimycobacterial drugs treated Mycobacterium JLS using atomic force microscopy. Chem Commun (Camb). 2009;(45):7021-7023. https://doi.org/10.1039/b914605a

21. Zhang X, et al. Machine learning analysis of AFM images reveals mechanical biomarkers of mycobacterial drug tolerance. Biophys J. 2022;121(3):467-479.

22. Kilfoil ML, et al. AI-assisted tracking of single bacteria in livecell microscopy. Cell Rep Methods. 2022;2(3):100201.

23. Chen WC, Chang CC, Lin YE. Pulmonary tuberculosis diagnosis using an intelligent microscopy scanner and image recognition model for improved acid-fast bacilli detection in smears. Diagnostics (Basel). 2024;14(16):1787. https://doi.org/10.3390/microorganisms12081734

24. English P, Morrison MJ, Mathison B, Enrico E, Shean R, O'Fallon B, Rupp D, Knight K, Rangel A, Gilivary J, Vance A, Hatch H, Lin L, Ng DP, Shakir SM. Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens. Microbiol Spectr. 2025 Aug 5;13(8):e0060225. https://doi.org/10.1128/spectrum.00602-25

25. Fu HT, Tu HZ, Lee HS, Lin YE, Lin CW. Evaluation of an AI-Based TB AFB Smear Screening System for Laboratory Diagnosis on Routine Practice. Sensors (Basel). 2022 Nov 4;22(21):8497. https://doi.org/10.3390/s22218497

26. Mbulayi O, Djungu SJ, Aketi L, Koulali MA, Azzaoui H, Koulali R, El Mzibri M, Chaoui I, Tayalati Y. Tuberculosis diagnosis using artificial intelligence: current trends and future prospects. Front Med (Lausanne). 2026 Jan 7;12:1569615. https://doi.org/10.3389/fmed.2025.1569615 PMID: 41585266; PMCID: PMC12825227

27. World Health Organization. (2021). Determining the local calibration of computer-assisted detection (CAD) thresholds and other parameters: A toolkit to support the effective use of CAD for TB screening. https://apps.who.int/iris/handle/10665/345925

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Published

2026-05-11

Issue

Vol. 54 No. 1 (2026)

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Articles

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Copyright (c) 2026 Svetoslav Yordanov, Yuliyana Atanasova (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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How to Cite

(1)
Yordanov, S.; Atanasova, Y. CONTEMPORARY APPROACHES IN THE DIAGNOSIS AND STUDY OF MYCOBACTERIA: THE ROLE OF MICROSCOPY AND ARTIFICIAL INTELLIGENCE. Probl Infect Parasit Dis 2026, 54 (1), 54-58. https://doi.org/10.58395/h9830j27.